Graph Embeddings Courses and Certifications

ArangoDB

Graph Embeddings with AQL for Product Recommendations - Session 2.6

Discover how to generate Amazon product recommendations using graph embeddings and AQL, with practical demonstrations of industrial applications and machine learning pipelines.

• YouTube
• 20 minutes
• On-Demand
• Free Video

Discriminative Prototype Selection for Graph Embedding

Explore graph embedding techniques, focusing on discriminative prototype selection methods for improved graph representation and matching in various applications.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Biais et discrimination en IA

Explorez les aspects techniques et sociaux des biais, de la discrimination et de l'équité dans l'IA, en mettant l'accent sur les impacts liés au sexe, à la race et à la situation socio-économique dans les modèles prédictifs.

• edX
• 5 weeks, 19-21 hours a week
• On-Demand
• Free Online Course (Audit)

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

Explore advanced techniques for detecting ideological leanings in social media through multi-task learning and graph embedding methods focused on Twitter data analysis.

• XuetangX
• On-Demand
• Free Online Course

IEEE

Inapproximability of Clustering in Lp Metrics

Explores computational complexity of clustering in Lp metrics, discussing approximation algorithms, hardness of approximation, and key concepts like Johnson Coverage Hypothesis.

• YouTube
• 21 minutes
• On-Demand
• Conference Talk

Databricks

Building a Knowledge Graph with Spark and NLP for Novel Drug Recommendations

Explore drug discovery using knowledge graphs, NLP, and Spark. Learn techniques for efficient data processing, entity recognition, and relationship extraction to enhance pharmaceutical research.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Aleksa Gordić - The AI Epiphany

Graph Convolutional Networks - GNN Paper Explained

Comprehensive exploration of Graph Convolutional Networks, covering theory, implementation, and applications. Delves into spectral methods, Weisfeiler-Lehman perspective, and GNN depth, offering insights for both beginners and experts.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Bite-Sized Neo4j for Data Scientists

Learn Neo4j for data science: connect to databases, use Python drivers, write Cypher queries, import data, perform graph analytics, and compare with SQL databases.

• YouTube
• 2 hours 30 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Task-Driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces

Explore task-driven network discovery using deep reinforcement learning on embedded spaces. Learn about improving network observability and optimizing data collection for accurate analysis.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Open Data Science

Graph Embeddings - Ways Your AI Can Learn From Your Connected Data - Nicolas Rouyer

Explore five ways AI can leverage graph embeddings to extract insights from connected data, enhancing machine learning algorithms and unlocking hidden patterns in complex relationships.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Devoxx

Leveraging Graphs for AI

Explore graph theory's role in AI, from data structures to machine learning, with practical examples and applications in analytics, algorithms, and neural networks.

• YouTube
• 57 minutes
• On-Demand
• Conference Talk

Angel 3.0 - A Full Stack Machine Learning Platform

Explore Angel 3.0, a comprehensive machine learning platform featuring auto feature engineering, hyperparameter tuning, model serving, and PyTorch integration for enhanced graph embedding and GNN algorithms.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Mervin Praison

GraphRAG: The Most Incredible RAG Strategy Revealed

Explore GraphRAG, an advanced retrieval-augmented generation system enhancing AI responses with relevant context. Learn implementation, setup, and advantages over traditional methods.

• YouTube
• 11 minutes
• On-Demand
• Free Video

BIMSA

Heterogeneous Graph Neural Network for Representation Learning

Explore heterogeneous graph neural networks for effective representation learning in complex graphs with diverse node types, edge types, and associated content.

• YouTube
• 50 minutes
• On-Demand
• Free Video

BIMSA

Geometric Deep Graph Learning: Exploring Opportunities in Different Geometric Spaces

Explore geometric deep graph learning in non-Euclidean spaces. Discover properties of hyperbolic and hyperspherical spaces for enhanced graph representation and analysis across various applications.

• YouTube
• 1 hour
• On-Demand
• Free Video